Answering Data Questions with Polars in Python

Import polars, configure table display and string length, load the IMDb dataset from csv, drop the index column, and convert the release date to date for analysis.

Identify the longest movie by runtime using Polars in Python, drop nulls, select title and runtime, sort descending, and convert minutes to hours to reveal 5.6 hours.

Compute the average movie runtime with Polars in Python by selecting the runtime column, dropping nulls, and converting minutes to hours to reveal 1.78 hours.

Select title and budget, drop nulls, and sort by budget to identify the most expensive movie: Pirates of the Caribbean On Stranger Tides at 380 million USD.

Compute the median budget for the movies by selecting the budget column and applying dot median in the data frame. Shows 15 million USD as the median budget.

answer data questions with polars in python by extracting the release month, grouping by month, counting releases, and identifying September as the top month with 588 movies.

determine which day of the week has the most releases using polars in python by modifying the code to compute weekday, revealing friday with 2053 releases.

Reuse prior code to compare day-of-the-week release with average ratings using polars, convert to pandas, and visualize with a scatter plot; find no clear relationship.

Load the Polars data frame, compute decades from release years, group by decade to count movies, and visualize the results with a bar plot.

Identify the most profitable movie using polars in python by selecting title, revenue, and budget; create a profit column, sort by profit, and reveal Avatar.

Identify the least profitable movie by sorting revenue and budget data using Polars in Python; the Lone Ranger shows the largest loss at 165 million USD.

Explore whether budget and revenue correlate by filtering nulls in the data frame, selecting budget and revenue, and plotting a scatter plot; conclude there is no clear correlation.

Filter the data frame to identify movies with profit of ten times or more of their budget, while removing zero-budget entries, revealing 243 qualifying films.

Use polars in Python to select the title and director, drop nulls, group by director, count films, and list the top five directors: Spielberg, Allen, Eastwood, Scorsese, Scott.

Use Polars in Python to group by director, sum profits, alias the profit column, and obtain the top five directors by profit.

Extract unique directors for the top five genres by filtering to the director column. The results include names like Tadeo Garcia, Jesse Nelson, Mel Gibson, and Nicholas Hunter.

In Polars for Python, filter the genres column to include rows containing thriller, using a chained approach to reveal all thriller-associated entries (about 1259).

Analyze title changes in a movie dataset with Polars in Python by comparing title and original title, filtering where they differ, and viewing translations.

Filter the title column in Polars for digits using a regex, display the resulting data frame, and verify titles like Spider-Man 3 and Death Race 2000 contain digits.

Use Polars in Python to select the title column, compute its length, sort by length descending, and identify the longest movie title.

Display the data frame, use polars to select the original language, drop nulls, filter where language equals english, and count the results to reveal 4505 english movies.

Answer the second language question using Polars in Python by calculating the proportion of English-language movies, dividing the English count by dataframe length to reveal 93%.

Inspect a data frame to reveal language distribution, using the original language column and value_counts with sort enabled to display languages in descending order, highlighting English as most represented.

Answer the first data question by computing summary statistics for popularity and filtering a two-column frame to reveal that Minions is the most popular movie in the dataset.

Compute the 75th percentile of popularity, then filter for movies at or above that threshold, returning titles such as Avatar and The Dark Knight Rises.

Answer three standard deviations above popularity's mean in Polars using Python by computing mean, standard deviation, and summary statistics, then filter titles such as Avatar and Pirates of the Caribbean.

Learn to answer data questions with Polars in Python by chaining a dataframe, selecting title and cast, and filtering where the cast contains the actor to reveal relevant movie titles.

Use Polars in Python to answer cast questions by selecting title and cast, then filter for Bradley Cooper and Jennifer Lawrence to identify their shared films.

Use Polars in Python to filter by title and fetch the release date for Catch Me If You Can; it was released on December 25, 2002.

Compute profit by subtracting budget from revenue in polars in python, create a profit column, and select relevant fields, revealing catch me if you can earned about 300 million USD.

Reuse the prior code to answer the third data question about my favorite movie, Catch Me If You Can, and show Steven Spielberg as the director.

Use polars in python to answer the favorite movie question by extracting and displaying the cast for Catch Me If You Can, including Leonardo DiCaprio and Tom Hanks.

Replace the director column with vote average to answer the favorite movie rating question using polars in python. The example shows Catch Me If You Can has a rating of 7.7.

Calculate the popularity of Catch Me If You Can and compare it to the dataset mean with Polars in Python, then create a boolean column Popularity above average.

Answer data questions with polars by extracting the tagline column; the tagline for Catch Me If You Can is the true story of a real fake.

Use Polars in Python to convert movie runtimes from minutes to hours, select title and runtime, and reveal Catch Me If You Can is 2.5 hours.

Load the dataset with Polars in Python from JSON, set display ten rows and 30-character strings, and inspect a 14,782-row, ten-column dataframe with channel_id, text, time, author, channel, and votes.

Use polars in python to count how often each author appears, build an author count column, and filter for comments by users who posted more than once.

Learn to count unique editors of comments in a Polars for Python dataset by filtering for edited time stamps, selecting unique authors, and obtaining the final count of 891.

Compute the most frequent commenter by grouping by author, counting comments, sorting in descending order, and selecting the top row to reveal Janet Stanley with 61 comments.

Use Polars in Python to count comments containing love by filtering the text column and applying lowercase for case-insensitive matching, then compute the height to reveal 664 comments.

Find the longest comment in a Polars in Python data frame by creating a length column from the text, sorting by length in descending order, and selecting the top row.

Learn to identify the most liked comment in Polars by converting the votes from string to numeric, handling k suffix, sorting descending, and extracting the top comment text.

Explore answering a data question with polars in python by filtering comments with more than 1,000 likes and calculating their proportion using the dataframe height and total length.

Answer question three by calculating the average number of likes per comment using Polars in Python, selecting the votes column, and showing the result as 11 likes.

Learn to answer data questions with Polars in Python by grouping by author, computing the average likes per comment, and extracting the top ten authors by this metric.

Learn to answer time questions in Polars using Python by extracting time-ago values with regex, building per-frame columns, and identifying the most recent comment.

Learn to answer time questions in polars for python by identifying the earliest comment using a time past column, filtering on years ago, and sorting to reveal the first post.

Clean the time column in a polars dataframe by removing the word edited, group by time, count occurrences, convert to pandas, and plot a bar plot of comments per year.

Learn to compute time-based proportions with Polars in Python by creating years, months, weeks, days, and hours ago columns, counting values, and deriving percentage shares for a dataset.

Explore how to compute the proportion of comments that are replies using a boolean reply column in a Polars dataframe, filtering and dividing by the total length to yield 29%.

Use polars to group by author, aggregate a count, sort by author count in descending order, and identify the top author, S_87, with 58 replies.

Identify the top five authors by reply count using Polars in Python, by applying top_k on author counts and selecting five.

Use polars in python to convert the votes column from string to integer, sort by votes in descending order, and extract the top text as the most liked reply.

Compute the average number of likes per reply using Polars in Python, refining existing code to select the relevant data and produce an average of two likes per reply.

Demonstrate filtering for reply rows to reveal the first comment on the video, by reusing my code. The first reply says thank you, with many thanks.

Create a bar plot of the top five most engaged viewers by comment count using polars in python, then style it with matplotlib and add a signature by Jora Mutange.

Load and explore the massive Stack Overflow survey data set with Polars in Python, inspect 89,184 rows and 84 columns, and focus on key columns to answer questions.

Learn to answer data questions with Polars in Python by counting developers in a data frame, filtering for 'I am a developer', and retrieving a count of 67,237.

Use Polars in Python to compute the proportion of people who code as a hobby, using value counts, then filter for hobby and convert to a percentage.

Compute the proportion of professional developers who work remotely using Polars in Python by filtering the main branch and remote work columns, counting rows, and calculating a percentage.

Using Polars in Python, this lecture answers profession question 4 by filtering for hobby, grouping by education level, counting, and sorting to show secondary school as the top education level.

Use Polars in Python to clean the yes code column, cast to integers, compute average years of coding by profession, and reveal senior executive as having the highest coding experience.

Compute the median salary from the survey data using Polars in Python by cleaning the comp yearly column, casting to integer and replacing Na with zero to yield 10,905.

Using Polars in Python, this lesson filters salaries by the 90th percentile and counts the number of people in that top bracket, showing 8913 individuals.

Use Polars in Python to filter United States data and select salary column; return top value showing highest pay for a professional developer in United States, 11 million usd.

Use polars in python to answer data scientist salary questions by reusing and tweaking code to find the highest industry salary, with manufacturing and transportation topping at nine million.

Answer the age question by listing age groups and counts using a one-liner with df.age.value_counts, revealing groups like 65+ with 1171, 55–64 with 3392, and under 18 with 4128.

Use polars in python to filter the age column for 65 years or older, then compute the dataset length and convert the result to 1.31%.

Add a language have worked with column in the data frame, filter under 18 years old. Compute the Python percentage among under 18 years old, showing 68 percent.

Use Polars in Python to find under 18 with coding experience at least the median for 25–34 year olds, after cleaning year codes and chaining filters.

Load a Polars dataframe in Python, filter for rust in the language to work with column, and compute the percentage of people wanting to work with rust in future, 29.94%.

Using Polars in Python, compare Rust usage between hobbyists and professional developers; Rust is more popular among hobbyists (27.17%) than professionals (12%).

Demonstrates using Polars in Python to compute the median years of coding experience for JavaScript developers, by selecting relevant columns, converting strings to numbers, and handling Na values.

Using Polars in Python, this lesson creates Python and R indicators, filters out dual knowledge, counts non-null values, and shows Python as more popular among data scientists.

Use Polars in Python to chain filter on the country column by a G7 list and show the unique G7 countries.

Using polars in python, filter the data frame for g7 countries, convert the salary column from string to numeric, and compute mean and median salaries by country.

Learn to answer country question 3 with Polars in Python by filtering for India and Python, then calculate the India Python knowledge percentage of 49.65%.

Apply the modified code to group by country, count Ruby and Go knowledge, and extract the top five countries (USA, Germany, UK, Canada, France) by highest counts.

Learn to answer which country has most primary and elementary students who know python using Polars in Python, and master debugging a chained workflow to reveal United States of America.

Apply Polars in Python to count languages per respondent, group by country, and sort to show the United States of America has the most people knowing the most programming languages.

Use polars in python to filter for the philippines, convert coding experience to numeric, and calculate the standard deviation, yielding 7.88.

Explore how to use Polars in Python to filter data scientists, group by education level, and count results, revealing that master's degree is the most represented education level.

Compute the median salary for data scientists in the United States using Polars in Python by filtering to the country and converting the salary column to numeric, yielding 130,000 USD.

Filter the education level to isolate data scientists in the United States with a master's degree and compute the median salary, revealing $132,000 versus $130,000, and illustrating a master's premium.

Use polars in python to identify data scientists without a degree and compute their percentage, revealing that about 1.63% lack a degree.

Use Polars in Python to filter a two-column dataframe by Jade in education level and Ruby in language, then count lawyers who code in Ruby, yielding 178.

Prepare database usage data using Polars in Python to build a pie chart, counting response IDs for Oracle, MySQL, and Microsoft SQL Server, then concatenate into a grouped data frame.

Create a beautiful pie chart showing database usage for MySQL, Microsoft SQL Server, and Oracle, converting Polars to pandas and styling with matplotlib.

Finish this course by embracing learning as a gift, a skill, and a choice, and celebrate your progress in answering data questions with polars in python.